Making channel hopping sequences for self-organized mobile networks

2013 ◽  
Author(s):  
Widist Bekulu Tessema ◽  
Keuchul Cho ◽  
Gihyuk Seong ◽  
Gisu Park ◽  
Kijun Han
Keyword(s):  
Mobile Networks ◽  
Self Organized ◽  
Channel Hopping

scholarly journals D2D Mobile Relaying Meets NOMA—Part II: A Reinforcement Learning Perspective

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1755
Author(s):  
Safaa Driouech ◽  
Essaid Sabir ◽  
Mounir Ghogho ◽  
El-Mehdi Amhoud
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Mobile Networks ◽  
Mixed Strategies ◽  
Channel Fading ◽  
Equilibrium Strategies ◽  
Self Organized ◽  
Satisfactory Performance ◽  
Communication Paradigm ◽  
Learning Schemes

Structureless communications such as Device-to-Device (D2D) relaying are undeniably of paramount importance to improving the performance of today’s mobile networks. Such a communication paradigm requires a certain level of intelligence at the device level, thereby allowing it to interact with the environment and make proper decisions. However, decentralizing decision-making may induce paradoxical outcomes, resulting in a drop in performance, which sustains the design of self-organizing yet efficient systems. We propose that each device decides either to directly connect to the eNodeB or get access via another device through a D2D link. In the first part of this article, we describe a biform game framework to analyze the proposed self-organized system’s performance, under pure and mixed strategies. We use two reinforcement learning (RL) algorithms, enabling devices to self-organize and learn their pure/mixed equilibrium strategies in a fully distributed fashion. Decentralized RL algorithms are shown to play an important role in allowing devices to be self-organized and reach satisfactory performance with incomplete information or even under uncertainties. We point out through a simulation the importance of D2D relaying and assess how our learning schemes perform under slow/fast channel fading.

scholarly journals The comparison of Self-organized map and k-means algorithms used for 4G network performance evaluation

2021 ◽  
Author(s):  
Shaoxuan Wang
Keyword(s):  
Performance Evaluation ◽  
Mobile Networks ◽  
Network Performance ◽  
Clustering Algorithms ◽  
Correlation Coefficients ◽  
Clustering Methods ◽  
Data Set ◽  
Self Organized ◽  
Degree Correlation ◽  
Significant Difference

Abstract With the increasing complexity of mobile networks, it has become more and more difficult to perform effective management of mobile networks, which has led to more data to be evaluated and optimized. This article focuses on the performance evaluation of Long Term Evolution (LTE) networks by using two unsupervised learning techniques. Besides, this paper aims to identify the pros and cons of these two clustering algorithms as well. To achieve the above goals, different dimensional datasets for learning a process based on two classic unsupervised clustering methods are introduced to this work. A Self-organized map (SOM) neural network and k-means are as a comparison algorithm and the sample data with three different degree correlation coefficients features with 63 LTE cells, which is from a major European city. The purpose behind using these two methods is to see how different dimensions of the datasets can be used for testing clustering effectiveness and we propose a method based on the features extracted from key performance indicators (KPIs) and Euclidean distance is used as the evaluation standard for the distance between different clusters and samples within clusters. The comparing results show that k-means has a better cluster performance in low dimension data set, whereas the SOM’s performance unsatisfactory. On the other hand, the SOM’s clustering performance is better than k-means in high dimension and big data set and it could visualize results. It was verified that there is a significant difference in the obtained results using different clustering algorithms.

scholarly journals Design Methodology for Self-organized Mobile Networks Based

2016 ◽  
Vol 3 (7) ◽  
pp. 46 ◽  
Author(s):  
John Petearson Anzola ◽  
Sandro Javier Bolanos-Castro ◽  
Giovanny Mauricio Tarazona-Bermudez
Keyword(s):  
Mobile Networks ◽  
Design Methodology ◽  
Self Organized

scholarly journals Softwarization of Mobile Network Functions towards Agile and Energy Efficient 5G Architectures: A Survey

2017 ◽  
Vol 2017 ◽  
pp. 1-21 ◽  
Author(s):  
Dlamini Thembelihle ◽  
Michele Rossi ◽  
Daniele Munaretto
Keyword(s):  
Mobile Networks ◽  
Mobile Network ◽  
Mobile Systems ◽  
Network Function Virtualization ◽  
Network Function ◽  
Self Organized ◽  
Pros And Cons ◽  
Network Functions ◽  
Open Issues

Future mobile networks (MNs) are required to be flexible with minimal infrastructure complexity, unlike current ones that rely on proprietary network elements to offer their services. Moreover, they are expected to make use of renewable energy to decrease their carbon footprint and of virtualization technologies for improved adaptability and flexibility, thus resulting in green and self-organized systems. In this article, we discuss the application of software defined networking (SDN) and network function virtualization (NFV) technologies towards softwarization of the mobile network functions, taking into account different architectural proposals. In addition, we elaborate on whether mobile edge computing (MEC), a new architectural concept that uses NFV techniques, can enhance communication in 5G cellular networks, reducing latency due to its proximity deployment. Besides discussing existing techniques, expounding their pros and cons and comparing state-of-the-art architectural proposals, we examine the role of machine learning and data mining tools, analyzing their use within fully SDN- and NFV-enabled mobile systems. Finally, we outline the challenges and the open issues related to evolved packet core (EPC) and MEC architectures.

Problem behavior in autism spectrum disorders: A paradigmatic self-organized perspective of network structures

2019 ◽  
Vol 42 ◽  
Author(s):  
Lucio Tonello ◽  
Luca Giacobbi ◽  
Alberto Pettenon ◽  
Alessandro Scuotto ◽  
Massimo Cocchi ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Problem Behaviors ◽  
Autism Spectrum ◽  
The Self ◽  
Network Structures ◽  
Self Organized ◽  
Critical Equilibrium ◽  
Self Organized Criticality ◽  
Acute Agitation ◽  
Spectrum Disorders

AbstractAutism spectrum disorder (ASD) subjects can present temporary behaviors of acute agitation and aggressiveness, named problem behaviors. They have been shown to be consistent with the self-organized criticality (SOC), a model wherein occasionally occurring “catastrophic events” are necessary in order to maintain a self-organized “critical equilibrium.” The SOC can represent the psychopathology network structures and additionally suggests that they can be considered as self-organized systems.

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